---
title: "TextAnalytics"
output: 
  flexdashboard::flex_dashboard:
    theme: flatly
    source: embed
    vertical_layout: fill
---

```{r setup, include=FALSE}
library(flexdashboard)                          ## flexdashboard
library(tidyverse)                              ## niche R
library(tidygraph)                              ## tidy graph manipulation
library(ggraph)                                 ## grammar of graph graphics
library(RColorBrewer)                           ## palettes
library(tidyjson)                               ## building blocks for json
library(foreach)                                ## iterative R
library(purrr)                                  ## functional programming in R
library(stringr)                                ## working w/ strings
library(plotly)                                 ## interactive graphics in R
library(lubridate)                              ## working with dates

set.seed(88)

source('build_dat.R')                           ## does what is commented out directly below
# results_data <- read_rds('results_data.RDS')    ## results from Metacran
# metrics <- read_rds('metrics.RDS')              ## results from packagemetrics
# pkgtopics <- read_rds('pkg_topic_class.RDS')    ## results from topicmodels
# dat <- read_rds('ecosystem_data.RDS')           ## combined & cleaned

source('build_tg.R')                            ## does what is commented out directly below
# tg <- read_rds('tg.RDS')                        ## results from miniCRAN 

# ofInterest <- c('tm', 'tidytext', 'hunspell'    ## which 15 packages to highlight in our viz
#                 , 'cleanNLP', 'openNLP'
#                 , 'NLP', 'lda', 'topicmodels'
#                 , 'koRpus', 'quanteda', 'tokenizers'
#                 , 'ngram', 'wordcloud'
#                 , 'purrr', 'Rcpp')
# 
dg_tbl <- dat %>%                               ## data to use; create labels for viz
    distinct(package, .keep_all =T) %>%
    mutate(highlights = ifelse(package %in% ofInterest, package, NA))
```

Setup 
===============================================

Column  {data-width=350}
-----------------------------------------------

```{r ecoplot, echo=FALSE, message=FALSE, warning=FALSE}
# a tease plot for the talk
tg %>%
    activate(nodes) %>%                         ## work on nodes
    left_join(., dg_tbl %>%                     ## bolster nodes for plotting
                  select(package, topic, highlights)
              , by = c('name' = 'package')) %>%
    filter(!is.na(topic)) -> tg_topic           ## create obj

tg_topic_plot <- tg_topic %>%                   ## create plot with query_class together
    ggraph('mds') +                             ## igraph layout
    geom_edge_arc(aes(color = type)             ## edit edges
                  , alpha = .75
                  , show.legend =F) +
    geom_node_point(aes(color = as.factor(topic)) ## edit nodes
                    , show.legend =F ) +
    geom_node_text(aes(label = highlights)
                   , size =4, repel =T) +       ## annotate
    scale_color_brewer(palette = 'BuGn') +      ## palette
    ggtitle('Existing Relationships\nBetween Text Analytics Packages') +
    theme_graph(background = 'light grey'
                , title_size = 22)              ## look

tg_topic_plot
```

Column {.tabset .tabset-fade}  
-----------------------------------------------  

### Scope

We will focus on the subjects of natural language processing (NLP) and text mining. We will identify a few frameworks, describe a continuum of use cases, and attempt to get you started in your first Text Analytics (TA) project.

Though TA has been around for a number of years in `R`, we have seen expansive growth in both applications and package development in the last two years. TA projects often occupy several contradictory attitudes:

* __Micro <- - -> Macro__
    + We need to parse: characters, letters, words, ngrams, sentences, books, volumes etc. using tokenizers
* __Dirty <- - -> Clean__
    + We need to work with:  unstructured strings, json, batched .zip files, html, perhaps in different languages or represented in different flavors of ASCII/Unicode
* __Weak <- - -> Powerful__
    + We need to: count, summarize, calculate distances, create features, apply machine learning, send/receive using APIs

_The purpose of this dashboard is to provide resources to analysts understand these attitudes, underlying frameworks, & use cases before diving too deep._

### What is __not__ considered?

* Is `R` the best programming language for Text Analytics?

* The __too big for memory__ problem.

* How to... anything. (_I am not an expert in Text Analytics_)

* What __framework__ will win out?

* Any comparable topic areas (in size & growth) in `R`?

* Packages on `R-Forge`, `Bioconductor`, or `github` only

* Much, much more.

### Initial Setup

 Packages  

```
library(flexdashboard)                          ## flexdashboard
library(tidyverse)                              ## niche R
library(tidygraph)                              ## tidy graph manipulation
library(ggraph)                                 ## grammar of graph graphics
library(RColorBrewer)                           ## palettes
library(tidyjson)                               ## building blocks for json
library(foreach)                                ## iterative R
library(purrr)                                  ## functional programming in R
library(stringr)                                ## working w/ strings
library(plotly)                                 ## interactive graphics in R
library(lubridate)                              ## working with dates
library(seer)                                   ## search metacran
library(crandb)                                 ## query crandb
```

 Sourced Files  

```
results_data <- read_rds('results_data.RDS')    ## results from Metacran
metrics <- read_rds('metrics.RDS')              ## results from packagemetrics
pkgtopics <- read_rds('pkg_topic_class.RDS')    ## results from topicmodels
dat <- read_rds('ecosystem_data.RDS')           ## combined & cleaned
tg <- read_rds('tg.RDS')                        ## results from miniCRAN 

ofInterest <- c('tm', 'tidytext', 'hunspell'    ## packages to highlight
                , 'cleanNLP', 'openNLP'
                , 'NLP', 'lda', 'topicmodels'
                , 'koRpus', 'quanteda', 'tokenizers'
                , 'ngram', 'wordcloud'
                , 'purrr', 'Rcpp')

dg_tbl <- dat %>%                               ## data; create labels
    distinct(package, .keep_all =T) %>%
    left_join(., package_classification
              , by = c('package' = 'document')) %>%
    mutate(highlights = ifelse(package %in% ofInterest, package, NA))
```

Packages
===============================================

Column  {.tabset .tabset-fade}
-----------------------------------------------

### Task Views  

`CRAN` allows for the organization of packages into __Task Views__ and this is one of the first stops you should make if you want to understand any topic's ecosystem!    

* `CRAN`: https://cran.r-project.org/web/views/    

* `MRAN`: https://mran.microsoft.com/taskview/info/?NaturalLanguageProcessing  

* `Crantastic`: http://www.crantastic.org/task_views/NaturalLanguageProcessing    

* `Metacran`: https://www.r-pkg.org/ctv/NaturalLanguageProcessing     


### `ctv` Package 

The following uses `ctv` to get a `list` of packages for `NaturalLanguageProcessing`:    
```{r, echo=TRUE, message=FALSE, warning=FALSE}
library(ctv)                                    ## CRAN Task Views

rslts <- ctv::CRAN.views(repos='http://cran.us.r-project.org') %>% ## locate the global ctvlist
    as.list() %>%                               ## convert to list
    .[20] %>%                                   ## 20 represents NaturalLanguageProcessing
    purrr::flatten(.)                           ## flatten

nlp_pkgs <- rslts$packagelist$name %>% 
    as.list()                                   ## pull name from df of packages; as a list

nlp_pkgs[1:5]                                   ## first five packages

rm(nlp, rslts)                                  ## declutter
```
How many packages are in `NaturalLanguageProcessing`?  

```{r}
length(nlp_pkgs)                                ## length
```

### So many Packages...

![CRAN now has 10,000 R Packages - Jan. 2017](http://revolution-computing.typepad.com/.a/6a010534b1db25970b01b8d2594d25970c-800wi)


### Metacran Database  

`Metacran` maintains a database of `CRAN` Packages and offers a query capability. The code below uses the `nlp_pkgs` list to query the database and return the results as a data frame.    

```{r, echo=TRUE, message=FALSE, warning=FALSE}
library(seer)                                   ## search metacran
library(crandb)                                 ## query crandb
library(jsonlite)                               ## json

source('cran_utils.R')                          ## helper functions to query crandb

results <- DB_list_to_DF(nlp_pkgs) %>% 
    select(-document.id) %>%
    mutate(query_class = 'Task View')           ## query and return data frame of results

glimpse(results)
```

### Query w/ `seer`   

Let's combine and store three queries: `'text'`, `'text analytics'`, & `'natural language'` as `query_list`.  

How many did each return?    
```{r, echo=TRUE, message=FALSE, warning=FALSE}
t <- seer::see('text', size = 1000)             ## search
ta <- seer::see('text analytics', size = 1000)  ## search
nlp <- seer::see('natural language', size = 1000) ## search

## list
query_list <- list(t, ta, nlp)                  ## build list

purrr::map(query_list, function(x){             ## get total hits
    x$hits$total
})
```

```{r Growth Image, eval=FALSE, message=FALSE, warning=FALSE, include=FALSE}
# growth_data <- results_data %>%
#     separate(date
#              , c("date", "time")
#              , sep = "T"
#             ) %>%
#     mutate(date = as.Date(date, format = "%Y-%m-%d")
#            , date = floor_date(date, 'week')) %>%
#     select(-time) %>%
#     group_by(date, query_class) %>%
#     summarize(n = sum(n())) %>%
#     group_by(query_class) %>%
#     mutate(csum = cumsum(n))
# 
# gg_three <- ggplot(growth_data, aes(x = date, y = csum)) +
#     geom_line(aes(color = query_class))

# ggsave(gg_three, filename = "nlp_growth.png", width = 4, height = 4, units = 'in')
```

### TA Package Growth 

![Growth](nlp_growth.png)


Frameworks
===============================================

Column  {.tabset .tabset-fade}
-----------------------------------------------

### Comparative Example: Document-Term Matrix

A Document-Term Matrix is a mathematical representation of the frequency of terms per document. An approach generally:   

* identifies a target text to split apart
* splits it apart based on a set of rules
* count the number of terms, represented by columns, per document
* calculate summary statistics to help understand  

### tm (plus qdap)

```{r, echo=TRUE, message=FALSE, warning=FALSE}
library(tm)
# library(qdap) ## optional

desc <- Corpus(VectorSource(dg_tbl$description))
desc_dtm <- DocumentTermMatrix(desc)
inspect(desc_dtm)
```

### quanteda
```{r, echo=TRUE, message=FALSE, warning=FALSE}
library(quanteda)

desc <- quanteda::dfm(dg_tbl$description)
desc_dtm <- convert(desc, 'tm')
inspect(desc_dtm)
```

### tidytext
```{r, echo=TRUE, message=FALSE, warning=FALSE}
library(tidytext)

desc <- dg_tbl %>% 
    select(-topicterms) %>%
    tidytext::unnest_tokens(., input = description
                            , output = token) %>% 
    count(package, token)

desc_dtm <- desc %>% 
    cast_dtm(., document = package
             , term = token
             , value = n)

inspect(desc_dtm)
```

### cleanNLP

```{r, echo=TRUE, message=FALSE, warning=FALSE}
library(cleanNLP)
cleanNLP::init_tokenizers(locale = 'en')
# desc <- dg_tbl$description %>% run_annotators(as_strings =T) %>% get_token
# saveRDS(desc, 'desc_clnnlp.rds')
desc <- readr::read_rds('desc_clnnlp.rds')

desc_dtm <- desc %>% group_by(id) %>% 
    count(word) %>% ungroup %>%
    tidytext::cast_dtm(., document = id
             , term = word
             , value = n)
inspect(desc_dtm)
```

Results
===============================================

Column {.tabset .tabset-fade}
-----------------------------------------------

### Metacran

```{r, eval=FALSE, message=FALSE, warning=FALSE, include=FALSE}
# ## helper function
# ## get list of package names from query
# 
# query_pkgs <- purrr::map(query_list, get_package_names)  %>% unlist %>% unique
# new_to_results <- query_pkgs[!query_pkgs %in% nlp_pkgs]
# 
# query_results <- DB_list_to_DF(new_to_results) %>% select(-document.id) %>%
#     mutate(query_class = 'metacran')
# 
# results_data <- rbind(results, query_results) %>%
#     separate(maintainer
#              , c("maintainer", "email")
#              , sep = " <"
#             ) %>%
#     select(-email)
# 
# saveRDS(results_data, 'results_data.RDS')
```

```{r, echo=FALSE, message=FALSE, warning=FALSE}
results_data <- readr::read_rds('results_data.RDS')

DT::datatable(results_data, options = list(
  pageLength = 40
))
```

### packagemetrics  

```{r, echo=FALSE, message=FALSE, warning=FALSE}
## `packagemetrics Dataset for pkgs 
metrics_tbl <- readr::read_rds('metrics.RDS')   ## results from packagemetrics

DT::datatable(metrics_tbl, options = list(
  pageLength = 40
))
```

### topicmodels  

```{r, echo=FALSE, message=FALSE, warning=FALSE}
pkgtopics <- readr::read_rds('pkg_topic_class.RDS') ## sourced from

DT::datatable(pkgtopics, options = list(
    pageLength = 40
))
```

Use Cases
===============================================

Column {.tabset .tabset-fade}
-----------------------------------------------

### Related Fields

![Figure 2.1 - Practical Text Mining and Statistical Analysis for Non-Structured Text Data Applications
G. Miner, D. Delen, J. Elder, A. Fast, T. Hill, and R. Nisbet, Elsevier, January 2012](relatedfields.png)

### Seven Practice Areas

![Figure 2.3 - Practical Text Mining and Statistical Analysis for Non-Structured Text Data Applications
G. Miner, D. Delen, J. Elder, A. Fast, T. Hill, and R. Nisbet, Elsevier, January 2012](sevenareatasks.png)


Ecosystem Plots {.storyboard}
===============================================

### Dependencies in TextAnalytics Packages by `query_class`

```{r tg_mut_dep, message=FALSE, warning=FALSE, include=FALSE}
## setup data
tg %>% 
    activate(edges) %>%                         ## work on edges
    filter(type == 'Depends') %>%               ## filter on type
    activate(nodes) %>%                         ## work on nodes
    left_join(., dg_tbl %>%                     ## bolster nodes for plotting
                  select(package, query_class, dl_last_month, highlights, stars)
              , by = c('name' = 'package')) %>%
    filter(!is.na(query_class)) -> tg_mut_dep   ## create obj
```

```{r tg_mut_dep_circle, echo=FALSE, message=FALSE, warning=FALSE, fig.width=10}
## plot
tg_mut_dep_circle <- tg_mut_dep %>%             ## create plot with query_class together
    ggraph('linear', circular =T) +
    geom_edge_arc(alpha = .25) +
    geom_node_point(aes(color = query_class, size = dl_last_month)) +
    geom_node_text(aes(label = highlights), size = 5, repel = T) +
    theme_graph()

tg_mut_dep_circle
```

***   

`Task Views` only represent a small proportion of the overall packages in the ecosystem but have huge download numbers because of package dependencies.

`Metacran` Packages are also seeing great traction in certain areas.

### Dependencies again, with `stars` from github.

```{r tg_mut_dep_cloud, echo=FALSE, message=FALSE, warning=FALSE, fig.width=10}
tg_mut_dep_cloud <- tg_mut_dep %>%
    ggraph('kk') +
    geom_edge_density() +
    geom_node_point(aes(color = query_class, size = stars)) +
    geom_node_text(aes(label = highlights), size = 5, repel = T) +
    theme_graph()

tg_mut_dep_cloud
```

***  

Stars represent `favorites` for github users.

### All Relationships

```{r tg_mut_all, message=FALSE, warning=FALSE, include=FALSE}
tg %>% 
    activate(nodes) %>%                         ## work on nodes
    left_join(., dg_tbl %>%                     ## bolster nodes for plotting
                  select(package, query_class, highlights, dl_last_month, stars)
              , by = c('name' = 'package')) %>%
    filter(!is.na(query_class)) -> tg_mut_all   ## create obj
```

```{r tg_mut_all_snail, echo=FALSE, fig.width=12, message=FALSE, warning=FALSE}
## plot
tg_mut_all_snail<- tg_mut_all %>%             ## create plot with query_class together
    ggraph('linear', circular =F) +
    geom_edge_arc(aes(color = type), alpha = .25) +
    geom_node_point(aes(size = dl_last_month)) +
    geom_node_text(aes(label = highlights), size = 4, repel = T) +
    theme_graph()

tg_mut_all_snail
```

### Relationships between Packages that Import Tidyverse Packages

```{r tg_mut_tidy, message=FALSE, warning=FALSE, include=FALSE}
tg %>% 
    activate(nodes) %>%                         ## work on nodes
    left_join(., dg_tbl %>%                     ## bolster nodes for plotting
                  select(package, query_class
                         , highlights, published
                         , stars, tidyverse_happy)
              , by = c('name' = 'package')) %>%
    filter(!is.na(query_class)) -> tg_mut_tidy   ## create obj
```

```{r tg_mut_tidycloud, echo=FALSE, fig.width=14, message=FALSE, warning=FALSE}
## plot
tg_mut_tidycloud <- tg_mut_tidy %>%
    ggraph('grid') +
    geom_edge_density() +
    geom_node_point(aes(color = tidyverse_happy), show.legend = T) +
    geom_node_text(aes(label = highlights), size = 4, repel = T) +
    theme_graph() +
    facet_graph(~tidyverse_happy)

tg_mut_tidycloud
```

### Packages by topic

```{r tg_mut_topic, message=FALSE, warning=FALSE, include=FALSE}
tg %>% 
    activate(nodes) %>%                         ## work on nodes
    # filter(name %in% ofInterest) %>%
    left_join(., dg_tbl %>%                     ## bolster nodes for plotting
                  select(package, query_class
                         , highlights, published
                         , stars, tidyverse_happy
                         , has_vignette_build, maintainer
                         , topic, topicterms)
              , by = c('name' = 'package')) %>%
    filter(!is.na(query_class)) -> tg_mut_topic   ## create obj
```

```{r tg_mut_topic_facet, echo=FALSE, fig.width=14, message=FALSE, warning=FALSE}
tg_mut_topic_facet <- tg_mut_topic %>%
    ggraph('grid') +
    geom_edge_density() +
    geom_node_point(aes(color = as.factor(as.character(topicterms)))) +
    geom_node_text(aes(label = highlights)) +
    facet_graph(~as.factor(topic)) +
    theme_graph()

tg_mut_topic_facet
```

### Top Authors

```{r, message=FALSE, warning=FALSE, include=FALSE}
gg_one_dat <- results_tbl %>%
    select(name, maintainer) %>%
    group_by(maintainer) %>% 
    count(sort =T) %>%
    filter(n > 1) %>%
    ungroup %>%
    left_join(., results_tbl %>%
                  group_by(maintainer) %>%
                  do(pkgs = paste(.$name))) %>%
    rowwise %>%
    mutate(pkgs = unlist(pkgs) %>% paste(., collapse = ', '))
```

```{r gg_one, fig.width=12, message=FALSE, warning=FALSE}
gg_one <- gg_one_dat %>%
    filter(n >= 5) %>%
    ggplot(aes(x = reorder(maintainer, n), y = n)) +
    geom_bar(stat = 'identity') +
    geom_text(aes(y = n-(n-1), label = pkgs), size = 3, color = 'white', hjust = 0.095) +
    xlab('') +
    ylab('Packages') +
    coord_flip() +
    theme_minimal()

gg_one
```

### Downloads over Time
```{r, message=FALSE, warning=FALSE, include=FALSE}
downloads <- readr::read_rds('downloads.RDS')
```
```{r, echo=FALSE, message=FALSE, warning=FALSE}
download_data <- downloads %>% 
    mutate(
        date = floor_date(date, 'week')
    ) %>%
    group_by(query_class, date) %>%
    summarize(mean = mean(count, na.rm =T)
              , median = median(count, na.rm =T))
```
```{r gg_two, echo=FALSE, fig.width=14, message=FALSE, warning=FALSE}
gg_two <- ggplot(download_data, aes(x = date, y = mean)) +
    geom_line(aes(color = query_class))

gg_two
```

### Suggests Count by Topic

```{r, echo=FALSE, message=FALSE, warning=FALSE, fig.width=12}
dg_tbl %>% 
    ggplot() +
    geom_boxplot(aes(x = as.factor(topic), y = suggests_count
                     , fill = query_class)) +
    geom_label(aes(x = as.factor(topic), y = suggests_count, label = highlights)) +
    scale_fill_brewer('BuGn') +
    facet_wrap(~as.factor(topic), scales = 'free_x', ncol = 5)
```

### Reverse Count by Topic

```{r, echo=FALSE, message=FALSE, warning=FALSE, fig.width=12}
dg_tbl %>% 
    ggplot() +
    geom_boxplot(aes(x = as.factor(topic), y = reverse_count
                     , fill = query_class)) +
    geom_label(aes(x = as.factor(topic), y = suggests_count, label = highlights)) +
    scale_fill_brewer('BuGn') +
    facet_wrap(~as.factor(topic), scales = 'free', ncol = 5)
```

Future
===============================================

Column {.tabset .tabset-fade}
-----------------------------------------------

### Shiny

* How might we get a quick ecosystem snapshot via a TaskView and search query?
* How might we build a quick tinder-like response dataset to match Users to packages?
* How might we generate subsets of StackOverflow questions to deliver to first time users?

### Packages

* tools to classify CRAN Packages using topic modeling
* widget to recommend packages based on code/script so far

### Research  

* other task view areas for comparison
* all non task view packages
* who's who in package development
* scores for interopability
`sos` package

References 
===============================================

Row {.tabset .tabset-fade}
-----------------------------------------------  

### People

* [Gabor Csardi](https://github.com/gaborcsardi) - METACRAN
* [Andri de Vries](https://github.com/andrie)
* [Ken Benoit](https://github.com/kbenoit) - quanteda
* [Taylor Arnold](https://github.com/statsmaths) - cleanNLP
* [David Robinson](https://github.com/dgrtwo) - tidytext
* [Julia Silge](https://github.com/juliasilge) - tidytext
* [Kurt Hornik](https://translate.google.com/translate?hl=en&sl=de&u=https://de.wikipedia.org/wiki/Kurt_Hornik&prev=search) - Godfather
* [Tyler Rinker](https://github.com/trinker)
* [Jereon Ooms](https://github.com/jeroen)
* [Dmitriy Selivanov](https://github.com/dselivanov) - text2vec
* [rOpenSci Text Workshop Participants](http://textworkshop17.ropensci.org/#participants)

### Talks

* [useR2017](https://channel9.msdn.com/Events/useR-international-R-User-conferences/useR-International-R-User-2017-Conference)  
    + [tidytext](https://channel9.msdn.com/Events/useR-international-R-User-conferences/useR-International-R-User-2017-Conference/Text-mining-the-tidy-way)
    + [Navigating R Packages](https://channel9.msdn.com/Events/useR-international-R-User-conferences/useR-International-R-User-2017-Conference/Navigating-the-R-package-universe)
    + [untidy text analysis](https://channel9.msdn.com/Events/useR-international-R-User-conferences/useR-International-R-User-2017-Conference/Text-Analysis-and-Text-Mining-Using-R)
    + [tidy text analysis](https://channel9.msdn.com/Events/useR-international-R-User-conferences/useR-International-R-User-2017-Conference/A-Tidy-Data-Model-for-Natural-Language-Processing)
    + [intro to nlp pt. I](https://channel9.msdn.com/Events/useR-international-R-User-conferences/useR-International-R-User-2017-Conference/Introduction-to-Natural-Language-Processing-with-R)
    + [intro to nlp pt. II](https://channel9.msdn.com/Events/useR-international-R-User-conferences/useR-International-R-User-2017-Conference/Introduction-to-Natural-Language-Processing-with-R-II)
* Conversations
    + [Package Interoperability](https://github.com/ropensci/textworkshop17/issues/2)
    + [testworkshop17](https://github.com/ropensci/textworkshop17/issues)
    + [Text Interchange Format](https://github.com/ropensci/tif)
    + [Not always so in sync](https://github.com/dselivanov/text2vec/issues/146)

### Links

* [Network Graph Inspiration](https://www.slideshare.net/RevolutionAnalytics/the-network-structure-of-cran-2015-0702-final) 
* [CRANsearcher](https://github.com/RhoInc/CRANsearcher)  
* [Informs Article](https://www.informs.org/ORMS-Today/Public-Articles/June-Volume-39-Number-3/Text-analytics)
* [Practical Text Mining...Excerpt](http://cdn2.hubspot.net/hubfs/2176909/Whitepaper_The_Seven_Practice_Areas_of_Text_Analytics_Chapter_2_Excerpt.pdf?t=1467833009120)
* [Local Guide by UC_Rstats](http://uc-r.github.io/text_conversion)
* [Globl Vectors](https://github.com/stanfordnlp/GloVe)
* [quanteda quickstart](https://cran.r-project.org/web/packages/quanteda/vignettes/quickstart.html)
* [text2vec](http://text2vec.org/api.html)
* [nltk in Python](http://www.nltk.org/)
* [qdap](http://trinker.github.io/qdap/vignettes/qdap_vignette.html)
* [SpeedReader](https://github.com/matthewjdenny/SpeedReader)

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